Evidence map›Paper›PMID 33441727›Full record

ArticleScientific reports2021

Radiation-induced cell cycle perturbations: a computational tool validated with flow-cytometry data.

Leonardo Lonati, Sofia Barbieri, Isabella Guardamagna, Andrea Ottolenghi, Giorgio Baiocco

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

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  15. Monte Carlo methods to assess biological response to radiation in peripheral organs and in critical organs near the target.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2024
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Leonardo LonatiRadiation Biophysics and Radiobiology Group, Physics Department, University of Pavia, 27100, Pavia, IT, Italy. leonardo.lonati01@universitadipavia.it.
Sofia BarbieriRadiation Biophysics and Radiobiology Group, Physics Department, University of Pavia, 27100, Pavia, IT, Italy.
Isabella GuardamagnaRadiation Biophysics and Radiobiology Group, Physics Department, University of Pavia, 27100, Pavia, IT, Italy.
Andrea OttolenghiRadiation Biophysics and Radiobiology Group, Physics Department, University of Pavia, 27100, Pavia, IT, Italy.
Giorgio BaioccoRadiation Biophysics and Radiobiology Group, Physics Department, University of Pavia, 27100, Pavia, IT, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cell cycle progression can be studied with computational models that allow to describe and predict its perturbation by agents as ionizing radiation or drugs. Such models can then be integrated in tools for pre-clinical/clinical use, e.g. to optimize kinetically-based administration protocols of radiation therapy and chemotherapy. We present a deterministic compartmental model, specifically reproducing how cells that survive radiation exposure are distributed in the cell cycle as a function of dose and time after exposure. Model compartments represent the four cell-cycle phases, as a function of DNA content and time. A system of differential equations, whose parameters represent transition rates, division rate and DNA synthesis rate, describes the temporal evolution. Initial model inputs are data from unexposed cells in exponential growth. Perturbation is implemented as an alteration of model parameters that allows to best reproduce cell-cycle profiles post-irradiation. The model is validated with dedicated in vitro measurements on human lung fibroblasts (IMR90). Cells were irradiated with 2 and 5 Gy with a Varian 6 MV Clinac at IRCCS Maugeri. Flow cytometry analysis was performed at the RadBioPhys Laboratory (University of Pavia), obtaining cell percentages in each of the four phases in all studied conditions up to 72 h post-irradiation. Cells show early [Formula: see text]-phase block (increasing in duration as dose increases) and later [Formula: see text]-phase accumulation. For each condition, we identified the best sets of model parameters that lead to a good agreement between model and experimental data, varying transition rates from [Formula: see text]- to S- and from [Formula: see text]- to M-phase. This work offers a proof-of-concept validation of the new computational tool, opening to its future development and, in perspective, to its integration in a wider framework for clinical use.

Indexed as

Cell CycleCell Line, TumorComputational BiologyComputer SimulationFlow CytometryHumansRadiotherapy Planning, Computer-Assisted

Identifiers

PMID33441727
PMCPMC7806866

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.